Asphaltene deposition remains a significant operational challenge in the petroleum industry, requiring precise elemental characterization to understand formation mechanisms and develop mitigation strategies. This study utilizes Synchrotron Radiation X-ray Fluorescence (SR-XRF) at the SESAME facility to profile asphaltene deposits from four southwestern Iranian oil wells. To enhance technical soundness and data reliability, the experimental spectral window was optimized to overcome interferences inherent in long air-path measurements, specifically targeting the mitigation of low-energy atmospheric absorption and high-energy Rayleigh/Compton scattering. A critical spectroscopic evaluation was performed to resolve the spectral overlap between atmospheric Krypton (Kr) Kβ emission and the Strontium (Sr) Kα line, enabling a definitive identification of inorganic mineral inclusions. The analysis revealed complex elemental profiles comprising transition metals (Fe, Ni, Cu, Zn) and the heteroatom bromine (Br). Variations in nickel (Ni) intensity across samples suggest its role as a sample-intrinsic component, while high iron (Fe) levels indicate a mix of natural porphyrin complexes and extrinsic mineral contamination. These findings demonstrate that by resolving specific spectral artifacts, SR-XRF serves as a robust, non-destructive analytical tool for the characterization and proactive management of asphaltene-related challenges in petroleum production.

Applications of energy dispersive X-ray fluorescence (ED-XRF) spectrometry for trace analysis of arsenic (As) and lead (Pb) in foods are limited by spectral interference around characteristic emission lines. To overcome this limitation, various chemometric data modeling approaches can be applied; however, dataset characteristics and model structure can significantly impact these approaches in their ability to accurately relate spectral features to analyte concentrations. This study compared linear and nonlinear multivariate regression models (partial least squares regression (PLSR) and artificial neural network (ANN), respectively) to model the spectral overlap and enable simultaneous analysis of trace As and Pb in rice-based foods. Calibration standards were developed through pelletization of powdered rice (4.0 ± 0.2 g) spiked with As and Pb (∼0-600 µg kg[-1]). Standards were analyzed via ED-XRF and resultant spectra were used to develop calibration models utilizing PLSR and ANN modeling. To assess method accuracy and applicability in real-world scenarios, a certified reference material of rice, 11 commercial rice-based products, and 12 Pb-spiked commercial foods were analyzed. PLSR yielded stronger calibration performance (R[2]As = 0.98, R[2]Pb = 0.93) compared to ANN; however, ANN modeling achieved lower quantification limits for As in samples containing detectable and non-detectable Pb content (53 µg kg[-1] and 54 µg kg[-1], respectively). Both models achieved successful validation in the analysis of the certified reference material with errors of +12.7 % (PLSR) and -19.4 % (ANN) in the prediction of As. PLSR modeling demonstrated strong predictive capabilities in commercial rice-based samples containing non-detectable Pb content, obtaining a median absolute error of 10.7 % in As quantification and 8 of 11 samples yielding less than 20 % error. Conversely, in Pb-spiked commercial samples (76-417 µg kg[-1] Pb), the ANN outperformed PLSR, obtaining an average absolute error of 14.1 % with 8 of 12 samples yielding less than 20 % error in As determinations. PLSR modeling correctly identified low Pb content in samples containing non-detectable Pb content while also yielding 75 % accuracy in the classification of samples containing Pb levels above current regulatory limits, supporting its use as a tool for semi-quantitative screening of Pb content in rice/rice-based foods at levels aligning with current regulations. This study compared PLSR and ANN modeling in their ability to statistically model and overcome As/Pb spectral overlap in ED-XRF, highlighting their advantages and limitations in specific analytical scenarios and demonstrating their potential to enable trace As and Pb screening in rice-based foods.

Although Er[3+]-doped glasses have been shown to be capable of producing an excellent combination of near-infrared and visible emissions; a number of conventional glass matrices are hampered by low levels of solubility for rare earths, concentration quenching, and poor structural uniformity which result in lower than desired optical quality. Furthermore, there is still a lack of systematic understanding regarding how modifications to the structure of aluminosilicate glasses influence their spectroscopic properties. Therefore, this investigation sought to develop and optimize a new series of Er[3+] doped Al2O3-SiO2-CaO-Na2O aluminosilicate glasses for use as advanced photonic materials, while investigating the relationships between the structure of the glass, the local environment of the rare earth ions within the glass matrix and the overall luminescent response. The glasses were produced via the traditional melt-quenching process and analyzed using Fourier Transform Infrared (FT-IR), optical absorption spectroscopy, Judd-Ofelt (J-O) analysis, and photoluminescence (PL) spectroscopy. Analysis via FT-IR indicated an improvement in the connectivity of the glass structure due to enhancement of both Al-O and O-Si-O vibrational bands present in the glass upon incorporation of Er[3+]. XRD confirmed the amorphous nature of the samples, while FT-IR analysis showed that increasing Al2O3 enhances structural connectivity within interconnected AlO4 and SiO4 network units. Judd-Ofelt analysis demonstrated the parameter trend Ω2 > Ω4 > Ω6 with the A3 composition achieving the highest Ω2 value indicating elevated site asymmetry and stronger covalency around Er[3+]ion. Upon 380 nm excitation, the photoluminescence spectra exhibited weak blue, intense green and orange emissions corresponding to the ([2]H11/2 →[4]I15/2), ([4]S3/2 →[4]I15/2), and ([4]F9/2 →[4]I15/2) transitions, respectively, together with a prominent near-infrared emission around 1.55 μm assigned to the ([4]I13/2 →[4]I15/2) transition. Additionally, the Judd-Ofelt analysis showed that the A3 glass composition possessed the largest Ω2 value among all compositions investigated, indicating that it had a greater degree of local structural asymmetry and a stronger level of covalency surrounding each Er[3+] ion. PL spectral data further demonstrated a strong intensity of green emission that corresponds to the [4]S3/2→[4]I15/2 and [4]I13/2→[4]I15/2 transitions. The radiative lifetime, quantum efficiency, and additional radiative parameters measured for the A3 glass composition also demonstrated that it possessed superior optical properties compared to the other compositions studied. The superior luminescence observed for the A3 glass composition was attributed to a higher degree of dispersion of the rare earth ions throughout the glass matrix, lessened effects of concentration quenching, and an optimal degree of modification of the aluminosilicate network. Overall, these results demonstrate that optimized Er[3+] doped aluminosilicate glasses provide good combinations of structural and spectroscopic properties and could therefore serve as suitable materials for use in the production of optical fiber and wave guide amplifiers, integrated photonic circuits, eye safe lasers, optical sensors, visible up conversion devices, and future generations of wide band optical communication systems. Hence, the optimized A3 sample exhibited superior radiative properties and enhanced luminescence performance confirming its potential for optical fiber amplifiers, planar waveguides, and photonic systems.

Visible microspectrophotometry enables the non-destructive acquisition of spectral information from microscopic regions and constitutes a valuable tool for the experimental teaching of spectroscopy, microscopy, and optical instrumentation. This work presents the design, fabrication, and evaluation of a low-cost ocular microspectrophotometer (OMP), developed using 3D printing and designed for integration with conventional optical microscopes.The device incorporates a diffraction grating, optical apertures, and a CMOS sensor to acquire continuous spectra over the 433-827 nm wavelength range from microscopic regions approximately 40 µm in diameter. In addition, a Python-based algorithm was developed to convert spectral images into transmittance vectors and calibrate the pixel-to-wavelength relationship. The system achieved a spectral resolution of approximately 0.84 nm/pixel and was validated using reference optical filters.Its educational potential was demonstrated through the spectral characterization of the RGB subpixels of a smartphone LCD screen and the analysis of the transmittance of a plant leaf. These activities provide an effective means of introducing concepts such as additive color synthesis, spatial resolution, light-matter interaction, and digital image processing. The OMP constitutes an accessible and reproducible platform that integrates additive manufacturing, digital image processing, and hands-on experimentation for teaching spectroscopy, microscopy, and the design of scientific instrumentation.

Due to the prevalence of food fraud and its associated risks to human health, food safety and quality assurance systems must become more comprehensive, rapid, and accurate. Food fraud often involves contamination, which refers to the presence of harmful substances in food, including biological, chemical, and physical adulterants. Traditional detection methods, such as microbiological and chemical testing, are limited in sensitivity, are time-consuming, and often lack comprehensive analysis capabilities. This report presents the development of a hybrid Raman and laser-induced breakdown spectroscopy (LIBS) system, referred to as Hy-R-LIBS, for the quantitative analysis of food contaminants on fruit surfaces. In this work, the term hybrid denotes a system in which both spectroscopic modalities are co-housed within a single optical chassis, share a common optical path, and perform automated sequential acquisition on the same sample spot, enabling either independent or joint analysis of the resulting spectra. Raman spectroscopy provides molecular fingerprinting capability, while LIBS enables elemental analysis at low concentrations. The miniaturization of the combined system is feasible because Raman and LIBS share similar optical pathways. To overcome the inherently weak Raman scattering cross-section that limits sensitivity in portable systems, a surface-enhancement strategy based on silver nanoparticles (AgNPs) was employed, which improves both Raman scattering signals and LIBS plasma emission. The study demonstrated that AgNP-assisted surface enhancement improves both LIBS and Raman signals, as confirmed by the quantitative analysis of food contaminants on fruit peel. Pesticide and heavy -metal controls were used, and the limit of detection (LOD) for various contaminants was estimated. The results indicate the feasibility of the hybrid system and the enhancement achieved through surface-enhancement techniques. Overall, the Hy-R-LIBS system offers a promising approach for the quantitative analysis of food contaminants. The combination of these optical spectroscopic techniques enables comprehensive investigations of complex food matrices contaminated with both organic chemicals and metallic residues, providing valuable insights for food safety assurance and public health.

Singular Value Decomposition (SVD) is a primary tool for exploratory analysis and for separating signal from noise and background in spectroscopic data, but it does not account for baseline variation. Standard SVD can assign to baseline features singular values comparable in magnitude to those of genuine chemical components, obscuring the boundary between signal and noise in the singular value spectrum and complicating determination of the chemical rank. This Note describes a preprocessing-enhanced SVD algorithm in which a Fourier-domain first-derivative transformation is combined with weights derived from the noise present in the data before the decomposition is performed, suppressing baseline contributions without loss of true signal components. The abstract spectra and time-traces are returned to the original spectral and temporal domains by inverse transformation, and a sign-ambiguity resolution procedure based on correlation coefficients is included. The algorithm is demonstrated on both synthetic and experimental time-dependent FTIR spectroscopic data. Relative to standard SVD, the enhanced decomposition yields a flat noise floor beyond the retained components, confining the baseline contribution to the discarded subspace rather than dispersing it through the low-rank subspace, and the recovered abstract spectral and temporal vectors carry less baseline contamination. Although demonstrated with time-dependent FTIR data, the algorithm applies to any variable-dependent spectroscopic measurement that can be organized as a two-way matrix. The implementation is released as open-source software.

This study evaluates the performance of portable (P-NIR) and benchtop (B-NIR) NIR spectrometers in providing useful information for assessing key physicochemical parameters in fermentation-derived foods, including wines, beers, and vinegars. Using chemometric techniques such as PLS and PLS-DA, models were developed to predict and classify sample characteristics with high accuracy. The P-NIR device demonstrated comparable performance to the B-NIR spectrometer in specific applications, highlighting its potential for cost-effective on-site analyses without the need for sample preparation. Parameters such as acidity, pH, polyphenol content, and specific indicators like alcohol content and bitterness were effectively measured, while classification models successfully differentiated wine varieties, origins, and production methods. This study underscores the versatility and accessibility of P-NIR technology, paving the way for its broader adoption in quality control processes within the food industry.

Microplastics (MPs) have become increasingly common in nearly every natural environment. As their pervasiveness grows, so does the urgency to understand their abundance and origins. Raman spectroscopy has been used to identify MPs in natural environments. However, fluorescence from additives within MPs, other environmental matter, measurement substrates, or other factors can prevent MP identification. Polyethylene (PE) subtypes such as low-density, high-density, and ultra-high molecular weight polyethylene (LDPE, HDPE, and UHMWPE) have widely varying uses and origins (e.g. residential, industrial). In this work, we demonstrate a technique using time-correlated single photon counting (TCSPC) Raman spectroscopy to isolate the Raman signal of microplastics from competing fluorescence. Fluorescence suppression reduces spectral noise and improves quantitative spectral analysis. Decomposing Raman spectra using Voigt profiles enables quantification of PE crystallinity and estimation of its density. In artificially fluorescent and environmental samples, TCSPC Raman spectroscopy improved visualization of weak spectral features and calculation of PE crystallinity compared with continuous-wave (CW) Raman spectroscopy. For an environmental PE MP sample, CW Raman-derived crystallinity estimates often exceeded 100%. These non-physical values were attributed to a significant fluorescence background and low signal-to-noise ratio. In contrast, TCSPC measurements produced crystallinity estimates of 55-70% and 65-80% at two sampled locations on the MP. These results show the potential to quantitatively characterize MPs with TCSPC Raman spectroscopy. The ability to identify the subtype of PE MPs could assist in identifying the sources of microplastic pollution to protect natural environments.

Sum-frequency generation (SFG) spectroscopy is a powerful method for probing molecular composition and organization at interfaces. However, interpretation of SFG spectra in multicomponent systems is complicated because spectral intensities depend on both surface population and molecular orientation. This creates a challenge for analysis of the adsorbed population. Two-dimensional correlation spectroscopy (2D-COS) can aid in spectral interpretation, but molecular reorientation can distort spectral trends and lead to incorrect signs of the cross peaks. In this work, we investigate this problem first using model SFG spectra for a binary system in which the population and reorientation contributions are known, and in which the signal changes in a nonlinear manner with respect to the surface mole fraction. We describe an amplitude-ratio-based spectral reconstruction method where fitted vibrational mode amplitudes are used to calculate intra-component amplitude ratios relative to selected anchor modes. The corrected amplitudes are then used to reconstruct SFG spectra with reduced reorientation-induced artifacts. The reconstructed spectra show suppressed homospectral asynchronous features associated with intra-component reorientation and recover the expected heterospectral 2D-COS signs. The method is then applied to experimental SFG spectra of a binary mixture, further demonstrating that amplitude-ratio reconstruction provides a targeted approach for reducing reorientation artifacts and thereby enables population-based interpretation of SFG data.

Linear regression for quantitative analysis commonly uses a calibration model based on a large global sample set spanning a substantial domain of calibration sample variance. Because of the large variance, determining accurate analyte amounts present in new target prediction samples is challenging. Instead, calibration samples closely matrix-matched to each target sample can be selected to generate localized linear regression models for accurate target sample analyte predictions. The difficulty with local regression in analytical chemistry is how to select calibration samples that bracket each target sample over small respective ranges of innate matrix-matched measured responses and analyte amounts. Recently developed was a robust autonomous local sample selection algorithm called local adaptive fusion regression (LAFR) that can realize the two-goaled sample selection. The method is based on the physicochemical responsive integrated similarity measure (PRISM) algorithm to compute hundreds of similarity measures between a target sample and a global library sample set. Depending on the library size, LAFR can require extensive time-consuming calculations and this paper compares LAFR to an easier more intuitive computer-human hybrid technique using virtual reality (VR) for local model sample selection. Recently explored with VR were successful applications to various chemometric data analysis scenarios relying on the user's lifetime problem-solving training and inherent pattern recognition skills. Like LAFR, these studies and this study use PRISM to compute sample similarity measures. These similarity values are further rendered into VR sample glyph features providing intuitive visualization of sample matrix effects, i.e., based on glyph appearances, the user selects matrix-matched calibration samples in VR localized to each target sample. Presented are partial least squares (PLS) prediction results from LAFR and VR selected calibration sets localized to fifteen target samples across three near infrared (NIR) data sets. By using VR, prediction errors are generally the same or better than LAFR without the lengthy LAFR calculations. Also presented is a discussion on the Rashomon effect supporting the substantial differences between the LAFR and VR regression vector shapes and magnitudes but the models predict similarly. Included in the discussion is the inability to interpret the models.

Calibration by Proxy (CbPx) is applied to the inductively coupled plasma mass spectrometry analysis of seven commercial pipe tobacco samples. Five proxy elements are employed to construct a single calibration curve that applies to every analyte in the sample. Both the preparation of the calibration curve and the multielement analysis of each sample is performed from the analysis of just two solutions. The analysis of a standard reference material (tomato leaves) demonstrates excellent recoveries for 12 test elements, ranging from 77% (Ni) to 104% (Cu), with relative standard deviations averaging 6.6%. Detection limits are in the ppb range (µg analyte per kg leaf). Pipe tobacco samples containing between 0.04 and 700 mg analyte per kg leaf are analyzed for Al, As, Ba, Cd, Cu, Fe, Mn, Mo, Ni, Pb, V, and Zn.

Cavity ring-down spectroscopy (CRDS) enables highly sensitive detection of trace gases but typically requires high-speed, high-resolution digitizers for exponential fitting of the measured "ring-down" decay signals. This work investigates a simplified signal processing approach based on the Time-over-Threshold (ToT) method, using only threshold crossing times instead of full waveform acquisition. In contrast to the ToT method with static thresholds, dynamic threshold signals are employed to improve linearity and robustness with respect to signal amplitude variations. The proposed dynamic ToT method is implemented using a compact analog circuit and evaluated using a mid-infrared CRDS setup for CO2 measurement. Experimental results demonstrate good agreement with conventional fitting, with improved reliability compared to the ToT method with static thresholds and comparable linearity over a broad range of ring-down decay rates. These findings establish the dynamic ToT method as an accessible and scalable alternative for acquiring decay data in CRDS, particularly suited for simultaneous multi-channel or multi-wavelength measurements.

In this study, a simple procedure to estimate the molecular weight of high-density polyethylene over a very wide range of molecular weights was investigated using Raman spectroscopy with multivariate data analysis. High-density polyethylene samples with viscosity average molecular weights (Mv) [g/mol] ranging from 0.1 to 7.4 million were synthesized, and their Raman spectra in the 3200 to 100 cm[-1] region were measured. Using multivariate analysis to develop a calibration model derived from each spectrum and molecular weight, a high correlation coefficient was achieved between the predicted and actual values of the model. Thus, this study shows that Raman spectroscopic method can be employed for estimating the molecular weight of high-density polyethylene. Furthermore, Raman spectroscopy can be implemented in optical systems utilizing optical fibers, enabling operation with the measurement point separated from the main device even in harsh process environments, including high temperatures. Consequently, this technique is highly resistant to environmental conditions and is extremely useful as a measurement method applicable in any measurement environment. In practical applications, the spectroscopic technique will be selected according to the purpose and measurement environment, aiming for industrial application of this method.

Enhancing surgical precision through automated tissue identification requires overcoming the complexities of real operative scenes. Specifically, material detection is complicated by tissue superposition (e.g. thin layer of blood masks underlying structures), intricate anatomical geometries, and lighting instability. These factors manifest as the mixed pixel and spectral variability problems, which remain the foremost challenges for hyperspectral imaging (HSI) in clinical applications.In this context, this paper presents a feasibility study leveraging Visible and Near-Infrared HSI combined with hyperspectral unmixing algorithms to address these complexities. Ex-vivo bovine samples, comprising key orthopedic tissues such as blood, ligaments, cartilage, bone, and fat, were imaged using HSI in a manner that replicates real operative challenges. Subsequently, six state-of-the-art unmixing algorithms selected for their ability to simultaneously resolve mixed pixels and mitigate spectral variability effects are evaluated. Through a comparative analysis, model-based unmixing algorithms demonstrated optimal performance in producing abundance maps that accurately quantify the proportion of distinct tissue types, even within a single pixel subject to variability perturbations. These algorithms successfully decouple environmental variability, such as shadowing, illumination shifts, and shape spectral deformations, from actual material proportions, preventing altered spectral signatures from confusing true abundance quantification.

The honey peach (Prunus persica L.) is highly valued by consumers due to its distinctive flavor and nutritional richness. However, its quality is strongly influenced by the maturity stage, which directly affects the optimal harvest time and market value. To achieve rapid, non-destructive, and quantitative maturity assessment, hyperspectral imaging (HSI) was utilized to collect spectral data from samples across the visible-near-infrared (VIS-NIR) range. A comprehensive maturity index (CMI) was subsequently established by integrating key physicochemical parameters, including fruit weight, size, color, firmness, pH, moisture content, and soluble solids content (SSC). This index provided a holistic representation of maturity status. To address the limitation of a small sample size, a regression generative adversarial network (RGAN) was introduced. This model jointly generated spectral data and their corresponding CMI values. The generated samples exhibited high consistency with real samples in terms of spectral feature distribution, as visualized by t-distributed stochastic neighbor embedding (t-SNE), and CMI value distribution. Comparative evaluation of prediction models indicated that data augmentation with 800 generated samples enabled the convolutional neural network regression (CNNR) model to achieve optimal performance, with Rp of 0.952 and RMSEP of 0.187. This approach significantly improved predictive accuracy and generalization capability. In summary, the proposed method, which integrates HSI with RGAN for CMI prediction, allows for accurate quantification of honey peach maturity. And it offers an efficient and intelligent solution for maturity grading and quality control.